Classification with deeplearning : clean start vs continue training
If the task involves only apples, orange and peaches, you should use method 1. As the number of classes is small, the network cannot generalize well to all classes. As a side note, you should start with the pretrained weights of YOLO v3 as some classes of YOLO v3 may be fruits, which can help your model converge faster. 

If the number of classes is large, for example a hundred different fruits, you should use method 2.2 . The model should be able to generalize to all fruits and can converage faster as many fruits look the same. This is the case of transfer learning. In original YOLO v3 training, image net weights for dark net is used for the backbone network. It accelerates the training of YOLO v3.

For 2.1, it will not work as gradient descent will not consider the trained weight. The trained weights will be over written by the weights for peaches. 

For a recommended method, it depends on your class size. If the model will continue to add more classes, you should perhaps use 2.2 but only start using 2.2 when you have a considerable amount of classes. Hope I can help you.